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The purpose of the paper is to propose and demonstrate a novel approach for addressing the challenges of path planning and obstacle avoidance in the context of mobile robots (MR). The specific objectives and purposes outlined in the paper include: introducing a new methodology that combines Q-learning with dynamic reward to improve the efficiency of path planning and obstacle avoidance. Enhancing the navigation of MR through unfamiliar environments by reducing blind exploration and accelerating the convergence to optimal solutions and demonstrating through simulation results that the proposed method, dynamic reward-enhanced Q-learning (DRQL), outperforms existing approaches in terms of achieving convergence to an optimal action strategy more efficiently, requiring less time and improving path exploration with fewer steps and higher average rewards.

The design adopted in this paper to achieve its purposes involves the following key components: (1) Combination of Q-learning and dynamic reward: the paper’s design integrates Q-learning, a popular reinforcement learning technique, with dynamic reward mechanisms. This combination forms the foundation of the approach. Q-learning is used to learn and update the robot’s action-value function, while dynamic rewards are introduced to guide the robot’s actions effectively. (2) Data accumulation during navigation: when a MR navigates through an unfamiliar environment, it accumulates experience data. This data collection is a crucial part of the design, as it enables the robot to learn from its interactions with the environment. (3) Dynamic reward integration: dynamic reward mechanisms are integrated into the Q-learning process. These mechanisms provide feedback to the robot based on its actions, guiding it to make decisions that lead to better outcomes. Dynamic rewards help reduce blind exploration, which can be time-consuming and inefficient and promote faster convergence to optimal solutions. (4) Simulation-based evaluation: to assess the effectiveness of the proposed approach, the design includes a simulation-based evaluation. This evaluation uses simulated environments and scenarios to test the performance of the DRQL method. (5) Performance metrics: the design incorporates performance metrics to measure the success of the approach. These metrics likely include measures of convergence speed, exploration efficiency, the number of steps taken and the average rewards obtained during the robot’s navigation.

The findings of the paper can be summarized as follows: (1) Efficient path planning and obstacle avoidance: the paper’s proposed approach, DRQL, leads to more efficient path planning and obstacle avoidance for MR. This is achieved through the combination of Q-learning and dynamic reward mechanisms, which guide the robot’s actions effectively. (2) Faster convergence to optimal solutions: DRQL accelerates the convergence of the MR to optimal action strategies. Dynamic rewards help reduce the need for blind exploration, which typically consumes time and this results in a quicker attainment of optimal solutions. (3) Reduced exploration time: the integration of dynamic reward mechanisms significantly reduces the time required for exploration during navigation. This reduction in exploration time contributes to more efficient and quicker path planning. (4) Improved path exploration: the results from the simulations indicate that the DRQL method leads to improved path exploration in unknown environments. The robot takes fewer steps to reach its destination, which is a crucial indicator of efficiency. (5) Higher average rewards: the paper’s findings reveal that MR using DRQL receive higher average rewards during their navigation. This suggests that the proposed approach results in better decision-making and more successful navigation.

The paper’s originality stems from its unique combination of Q-learning and dynamic rewards, its focus on efficiency and speed in MR navigation and its ability to enhance path exploration and average rewards. These original contributions have the potential to advance the field of mobile robotics by addressing critical challenges in path planning and obstacle avoidance.

The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.

The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

The reemergence of immersive virtual technology (IVR) provides both opportunities and challenges for workplace learning (WPL). The purpose of this study is to explore and develop knowledge about how gamification influences the WPL experience by addressing two research questions: RQ1. What characterizes a gamified immersive safety training experience with IVR technology? and RQ2. How does gamified immersive safety training with IVR technology impact the WPL experience?

The study adopted a mixed methods approach by combining a systematic literature review with a case study on an empirical project about immersive fire safety training for train operators that are used at the Swedish train operating company SJ. The case study included data from semistructured interviews, Web survey and observation studies. The data was analyzed in two stages combining inductive and deductive data analysis for identifying themes and categories.

The findings of the study are twofold: (1) themes that conceptualize the gamified immersive safety training experience based on outputs from both the literature review and the first round of data analysis; and (2) a framework with three overarching categories that are mapped with the identified themes, and which were deduced throughout the second round of data analysis.

The originality of the findings stresses the implications of how a body of knowledge that synthesizes gamification concepts with immersive safety training, can inform the design of WPL experiences that are facilitated with IVR technology. As such, the implications of the findings are targeted toward both the advancement of the IVR discourse in the WPL field, but also toward practical considerations for design of immersive learning experiences that enrich WPL practices and culture.

This paper contributes to the field of public services’ performance measurement systems by proposing a benchmarking-based methodology that improves the effective use of big and open data in analyzing and evaluating efficiency, for supporting internal decision-making processes of public entities.

The proposed methodology uses data envelopment analysis in combination with a multivariate outlier detection algorithm—local outlier factor—to ensure the proper exploitation of the data available for efficiency evaluation in the presence of the multidimensional datasets with anomalous values that often characterize big and open data. An empirical implementation of the proposed methodology was conducted on waste management services provided in Italy.

The paper addresses the problem of misleading targets for entities that are erroneously deemed inefficient when applying data envelopment analysis to real-life datasets containing outliers. The proposed approach makes big and open data useful in evaluating relative efficiency, and it supports the development of performance-based strategies and policies by public entities from a data-driven public sector perspective.

Few empirical studies have explored how to make the use of big and open data more feasible for performance measurement systems in the public sector, addressing the challenges related to data quality and the need for analytical tools readily usable from a managerial perspective, given the poor diffusion of technical skills in public organizations. The paper fills this research gap by proposing a methodology that allows for exploiting the opportunities offered by big and open data for supporting internal decision-making processes within the public services context.

The purpose of this multi-voiced paper is to propose a prosocial paradigm for the field of performance management and management control systems. This new paradigm suggests cultivating prosocial behaviour and prosocial groups in organizations to simultaneously achieve the objectives of economic performance and sustainability.

The authors share a common concern about the future of humanity and nature. They challenge the influential assumption of economic man from neoclassical economic theory and build on evolutionary science and the core design principles of prosocial groups to develop a prosocial paradigm.

Findings are based on the premise of the prosocial paradigm that self-interested behaviour may outperform prosocial behaviour within a group but that prosocial groups outperform groups dominated by self-interest. The authors explore various dimensions of performance management from the prosocial perspective in the private and public sectors.

The authors call for theoretical, conceptual and empirical research that explores the prosocial paradigm. They invite any approach, including positivist, interpretive and critical research, as well as those using qualitative, quantitative and interventionist methods.

This paper offers implications from the prosocial paradigm for practitioners, particularly for executives and managers, policymakers and educators.

Adoption of the prosocial paradigm in research and practice shapes what the authors call the prosocial market economy. This is an aspired cultural evolution that functions with market competition yet systematically strengthens prosociality as a cultural norm in organizations, markets and society at large.

The purpose of this study is to shed light on the determinants of TikTok cyberaddiction during the COVID-19 pandemic, as well as the resulting attitudinal and intentional reactions of users.

An online survey of 305 young TikTok users was conducted to test the research hypotheses. Data was analyzed through a partial least sqaures approach.

The results highlight the mediating role of cyberaddiction to TikTok in the relationship between psychological disorders (anxiety and loneliness) and attitude toward TikTok. Indeed, it was found that anxiety and loneliness contribute to TikTok cyberaddiction. The latter would have a positive effect on attitude toward TikTok, which decreases in the presence of fear of missing out (FOMO). It was also proven that attitude toward TikTok has a positive impact on attitude toward the brands promoted on this platform, resulting in an increase in purchasing intentions.

This study was conducted during the COVID-19 pandemic. It is therefore interesting to replicate it to see if the research model can be still validated in the absence of an emergency situation. Testing the model on a larger target, including older TikTok users, could also lead to interesting findings.

Knowledge of the psychological factors behind cyberaddiction should not be seen as an opportunity to take advantage from addicted shoppers’ vulnerability. Managers should rather limit communication campaigns on TikTok during crisis periods, undertake retargeting actions by sparing vulnerable TikTok users and avoid stimulating anxiety and loneliness which can lead to cyberaddiction and compulsive purchases.

This study sheds light on the central role of cyberaddiction to TikTok in the relationship between psychological disorders and users’ reactions, during a pandemic. It focused on the moderating role of FOMO on the relationship between determinants of cyberaddiction and attitude toward TikTok. Escape theory and attachment theory were used and adapted to the context of social media.

El propósito de este estudio es arrojar luz sobre los determinantes de la ciberadicción a TikTok durante la pandemia de Covid-19, así como las reacciones actitudinales e intencionales resultantes de los usuarios.

Se realizó una encuesta en línea a 305 jóvenes usuarios de TikTok para probar las hipótesis de investigación. Los datos fueron analizados a través de un enfoque PLS.

Los resultados destacan el papel mediador de la ciberadicción a TikTok en la relación entre trastornos psicológicos (ansiedad y soledad) y la actitud hacia TikTok. De hecho, se encontró que la ansiedad y la soledad contribuyen a la ciberadicción a TikTok. Esta última tendría un efecto positivo en la actitud hacia TikTok, que disminuye en presencia del miedo a perderse algo (FOMO, por sus siglas en inglés). También se demostró que la actitud hacia TikTok tiene un impacto positivo en la actitud hacia las marcas promocionadas en esta plataforma, lo que resulta en un aumento en las intenciones de compra.

Este estudio arroja luz sobre el papel central de la ciberadicción a TikTok en la relación entre trastornos psicológicos y reacciones de los usuarios, durante una pandemia. Se centró en el papel moderador del FOMO en la relación entre determinantes de la ciberadicción y la actitud hacia TikTok. Se utilizaron y adaptaron la Teoría del Escape y la Teoría del Apego al contexto de las redes sociales.

Este estudio se llevó a cabo durante la pandemia de Covid-19. Por lo tanto, sería interesante replicarlo para ver si el modelo de investigación aún puede ser validado en ausencia de una situación de emergencia. Probar el modelo en un público objetivo más amplio, que incluya a usuarios mayores de TikTok, también podría conducir a hallazgos interesantes.

El conocimiento de los factores psicológicos detrás de la ciberadicción no debe verse como una oportunidad para aprovechar la vulnerabilidad de los compradores adictos. Los gerentes deberían más bien limitar las campañas de comunicación en TikTok durante los períodos de crisis, emprender acciones de retargeting evitando a los usuarios vulnerables de TikTok y evitar estimular la ansiedad y la soledad que pueden llevar a la ciberadicción y a compras compulsivas.

本研究旨在探索COVID-19大流行期间TikTok网络成瘾的决定因素, 以及用户的态度和意图反馈。

为了验证研究假设, 本研究对305位年轻的TikTok用户进行了在线调查, 并使用PLS方法对数据进行了分析。

研究结果凸显了在COVID-19大流行期间, TikTok网络成瘾与心理障碍（焦虑和孤独）之间的中介作用。实际上, 研究发现焦虑和孤独可能会促成TikTok网络成瘾的发展。而TikTok网络成瘾则可能在一定程度上改善对TikTok的态度, 减轻错失恐惧。研究还表明, 对TikTok的态度对该平台推广的品牌态度产生积极影响, 进而增加了购买意图。

这项研究详细探讨了COVID-19大流行期间TikTok网络成瘾的核心作用, 特别关注了心理障碍与用户反应之间的关系。重点分析了逃避理论和依恋理论在社交媒体环境下的应用, 并对错失恐惧在网络成瘾和对TikTok态度之间的调节作用进行了调查和分析。

这项研究是在COVID-19大流行期间进行的, 因此复制它以验证研究模型在紧急情况缺失的情况下是否仍然有效是很有趣的。将这个模型应用于更广泛的对象, 包括年长的TikTok用户, 可能会带来一些有趣的发现。

了解网络成瘾背后的心理因素是非常重要的, 但不应被视为利用沉迷购物者的脆弱性的机会。在危机期间, 管理者应该限制在TikTok上的传播活动, 以避免刺激焦虑和孤独, 保护那些脆弱的TikTok用户, 防止他们沉迷于平台并进行强迫性购物。

This study investigates the progress of the Broad-Based Black Economic Empowerment (B-BBEE) Act 2003, and its associated Chartered Accountancy Profession Sector Code. In doing so, we explore why B-BBEE affirmative action has not yet achieved the Code's representational intentions, including systemic subtleties inhibiting this success.

Using semi-structured interviews, we explore the lived experiences of Black aspirant Chartered Accountants (CAs) undertaking articles in global audit firms. The experiences are thematically analysed, embracing a comprehensive theoretical approach that encompasses professional and social closure, as well as boundary work, to adequately understand why affirmative transformational endeavours persistently face uphill tasks. The utilisation of multifaceted theorisation is deemed essential for a more nuanced portrayal of the intricacies inherent in the CA profession in South Africa.

The narratives presented by Black aspiring CAs unveil a complex web of exclusionary practices entrenched in institutionalised historical, professional, and social contexts. The multifaceted nature of closures, symbolised by racial, cultural, and linguistic factors, significantly impacts the experiences of Black trainees. The findings furthermore show that deliberate intervention beyond compliance with the Government’s framework is necessary for meaningful transformation.

The paper brings to the fore the current lived experiences of underrepresented Black CAs in global auditing firms. In doing so, these firms are empowered with incremental knowledge of the prevailing challenges and can thus make tangible improvements towards authentic transformation. Additionally, the results help in tracking the advancements made through affirmative action, acting as a feedback loop for future developments in transformation policy.

Contributing to the critical accounting literature, our study extends scholarship on the barriers faced by CAs and the limitations in their capacity to challenge these obstacles within global audit firms. We offer practical policy-focused recommendations that, if implemented, can address the complex socio-political realities obstructing the success of affirmative action. By sharing first-hand accounts, our study aims to empower auditing firms and other related stakeholders with actionable insights, enabling them to improve genuine inclusivity and foster equitable representation in the accounting profession.

This study aims to create an efficient approach to validate building energy simulation models amidst challenges from time-intensive data collection. Emphasizing precision in model calibration through strategic short-term data acquisition, the systematic framework targets critical adjustments using a strategically captured dataset. Leveraging metrics like Mean Bias Error (MBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)), this methodology aims to heighten energy efficiency assessment accuracy without lengthy data collection periods.

A standalone school and a campus facility were selected as case studies. Field investigations enabled precise energy modeling, emphasizing user-dependent parameters and compliance with standards. Simulation outputs were compared to short-term actual measurements, utilizing MBE and CV(RMSE) metrics, focusing on internal temperature and CO₂ levels. Energy bills and consumption data were scrutinized to verify natural gas and electricity usage against uncertain parameters.

Discrepancies between initial simulations and measurements were observed. Following adjustments, the standalone school 1’s average internal temperature increased from 19.5 °C to 21.3 °C, with MBE and CV(RMSE) aiding validation. Campus facilities exhibited complex variations, addressed by accounting for CO₂ levels and occupancy patterns, with similar metrics aiding validation. Revisions in lighting and electrical equipment schedules improved electricity consumption predictions. Verification of natural gas usage and monthly error rate calculations refined the simulation model.

This paper tackles Building Energy Simulation validation challenges due to data scarcity and time constraints. It proposes a strategic, short-term data collection method. It uses MBE and CV(RMSE) metrics for a comprehensive evaluation to ensure reliable energy efficiency predictions without extensive data collection.

The purpose of this paper is to study current practices in adopting blockchain technology amongst export companies in West Sweden and to capture their CEOs’ knowledge of and attitudes towards blockchains.

Factors enabling or hindering the adoption of blockchains were identified from a comprehensive literature review and a survey of 72 chief executive officers (CEOs) of export-oriented firms in West Sweden, all with turnovers exceeding €2m, regarding their knowledge of and attitudes towards blockchains.

Blockchain technology is not currently perceived to provide benefits that would outweigh the costs of introducing it into West Sweden’s export firms. Nevertheless, the findings suggest that such technology, though currently too immature to meet today’s industrial requirements, could experience more widespread use if certain key factors (i.e. lower cost, traceability, improved security or trustworthiness and new blockchain-enabled business models) are prioritised.

Answered by 72 CEOs, the survey achieved a response rate of 6%, meaning that the findings are only exploratory. Even so, they offer new insights into CEOs’ attitudes towards blockchain technology.

The CEOs reported comparatively limited knowledge of and experience with implementing blockchains, the lack of which has hampered their large-scale implementation in multi-actor supply chains.

Negative sentiment amongst CEOs towards blockchain technology may lower on-the-job satisfaction amongst tech personnel aspiring to develop and implement blockchain applications in their firms.

Knowledge of and attitudes towards blockchain technology amongst top-level managers, as well as about factors enabling or hindering its adoption, guide managers in crafting strategies for implementing blockchains in their organisations and maximising the benefits therein. Unlike past studies focussing on technological aspects or views of experts and middle-management, the study was designed to capture the views of CEOs.

This study aims to investigate different types of platform providers (PPs) to gain a deeper understanding of the characteristics and underlying logic of this group within collaborative consumption (CC). As CC occurs with three groups of actors (PP, peer service provider and customer) and is predominantly viewed from the customer perspective, this study offers insights from the under-researched PP perspective.

This research applies a multiple case study approach and analyzes descriptively and thematically 92 cases of CC PPs gathered through the Crunchbase database.

The authors derive four archetypes of CC PPs, namely, the hedonist, functionalist, environmentalist and connector, that differ in their offered values, dominating motives and activities across industries.

The authors conceptualize CC by clearly describing the four archetypes and their characteristics. However, further research would benefit from including databases other than Crunchbase.

PPs need to understand their value offerings and customer preferences to develop convincing value propositions and offer engaging activities. PPs would benefit from a more active social media presence to build strong relations with customers and peer service providers to effectively communicate their values.

The paper is pioneering as it encompasses the perspective of CC PPs and operationalizes the concept of CC. The authors address the lack of research on CC by conducting an extensive case study.